Embodiments described herein generally relate to medical image processing, and in particular to registering a medical image dataset with a reference medical image dataset.
In the medical field, two-dimensional (2D) and three-dimensional (3D) image datasets are collected by a variety of techniques—referred to as modalities in the field—including conventional X-ray, computer-assisted tomography (CT), magnetic resonance (MR), ultrasound and positron-emission-tomography (PET). Examples of 2D images include not only conventional X-ray images, but also 2D images derived from 3D image datasets, i.e. volume datasets, such as a “slice” of a CT scan or a “slab” of volume data from a CT scan in a multi-planar reformatting (MPR) view. Time resolved 3D studies are also well known and are usually referred to as 4D studies, with time being the fourth “dimension”. For example, time-resolved perfusion in abdominal organs is measured using 4D dynamic contrast enhanced CT (DCE-CT). It is also known to co-present images of the same patient taken with different modalities, such as combining CT and PET scans into a single image. These combined representations are sometimes referred to as 5D studies.
A common task when processing/reviewing medical image data is a desire to identify pixels/voxels in an image dataset that correspond with a particular feature of interest, for example a specific anatomical feature (object) under investigation. The method by which features of interest represented in image data are identified is referred to as segmentation. When medical image data has been segmented to identify feature(s) of interest, information regarding the parts of the data (e.g. in terms of specific pixels/voxels) corresponding with the feature(s) of interest may be referred to as segmentation data/information and may be recorded for later use.
Manual segmentation of image data may be performed by an expert viewing a representation of the data on a display and manually identifying objects of interest, for example by marking locations associated with an object of interest on the display through a user input device, such as a mouse or trackball.
Manual segmentation can typically provide highly reliable segmentation results in view of the sophistication of the expert in recognizing objects, such as a clinician identifying organs and their sub-structure in 2D or 3D X-ray images. However, a drawback of manual segmentation is that it can be a very labor-intensive process, and this can make it impractical where large numbers of datasets are being processed, for example as part of a large-scale comparative study. Another potential drawback of manual segmentation is the inherent subjectivity of the clinician and the need for trained individuals to perform the task reliably.
To help address these drawbacks, automated image processing procedures carried out by computers have been developed to automatically perform segmentation in a host of different applications.
FIG. 1 is a flow chart schematically showing the principles of a general approach to automatic segmentation.
In step R1, a novel image dataset (e.g. a patient image dataset under investigation) is provided to a computer for segmentation.
In step R2, a registration is carried out to align the novel image dataset with a reference image dataset which has already been segmented and stored along with its segmentation data. This alignment step is referred to in the prior art as registration. The output of registration is a spatial mapping between the voxels of the reference image dataset and the novel image dataset. The reference image dataset with its segmentation data is referred to as an atlas dataset, or simply an atlas. The atlas typically carries the segmentation data in a set of masks which indicate labeled features, such as one or more anatomical features that have been labeled manually. For example, in the case of a 3D medical image, the atlas has its anatomical regions of interest labeled, so that all voxels belonging to a particular anatomical region are marked with a common label.
In step R3, the spatial mapping computed in step R2 is applied to the atlas segmentation data, in such a way as to propagate the atlas segmentation data from the reference image dataset to the novel image dataset.
In step R4, the segmented novel image dataset, i.e. the novel image dataset and the associated segmentation data/information, is output. In the case of 3D medical image data, the novel image dataset may be loaded into and displayed by a volume rendering program, which may exploit the segmentation data to allow selective configuration of display parameters based on the segmentation data.
A key aspect to the performance of the of the approach of FIG. 1 is the extent to which the novel image dataset can be properly registered with the atlas dataset. There are various approaches to the registration process which are designed to optimize the overlap in features represented in the novel and atlas datasets following registration. Nonetheless, the wide variations in physiology between patients means it can be difficult to select an atlas dataset and registration process that can work equally well for all patients.
The process of registering a novel dataset with an atlas dataset will typically involve various transformations, such as rigid, non-rigid and warp transformations. One approach to dealing with the wide variations in patient physiology is to allow significant degrees of freedom in how the transformation to be used during the registration process may be applied. However, allowing too much freedom in the registration transformations can give rise to unrealistic results, for example by in effect allowing for the data to be over-fitted, and for tearing and other discontinuities to arise in the registered dataset.
An alternative approach to dealing with the potential for wide variations in patient physiology is to rely on multiple atlases. With a multi-atlas segmentation approach an appropriate atlas for a given novel dataset may be selected from among a plurality of atlases, for example based on the registration performance observed for different atlases (e.g. in effect selecting whichever atlas best matches the novel dataset). However, automated medical image data processing is a highly computer-intensive process, primarily because of the large amounts of data typically involved, and multi-atlas approaches to segmentation can significantly increase the amount of processing and memory required to process the image data. Furthermore, in some situations there may be a desire to map features of a number of novel study datasets to a “standard” atlas dataset, for example to allow for consistent comparison between a number of novel datasets. However, relying on different atlases for different novel datasets impacts the extent to which this can be reliably done.
In view of these drawbacks with existing approaches there is a need to provide improved schemes for medical image processing, and in particular to provide improved schemes for registering a medical image dataset with a reference medical image dataset.